import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


lowerCamelCase__  =					16
lowerCamelCase__  =					32


def 				lowerCAmelCase__       (			a__    ,     a__ = 16    ,     a__ = "bert-base-cased" )      ->Any:




  '''simple docstring'''

  _UpperCamelCase           =    AutoTokenizer.from_pretrained(a__ )
  _UpperCamelCase           =    load_dataset("glue"    ,     "mrpc" )

  def tokenize_function(a__ ):
    # max_length=None => use the model max length (it's actually the default)
    _UpperCamelCase           =    tokenizer(examples["sentence1"]    ,     examples["sentence2"]    ,     truncation=a__    ,     max_length=a__ )
    return outputs

  # Apply the method we just defined to all the examples in all the splits of the dataset
  _UpperCamelCase           =    datasets.map(
      a__    ,     batched=a__    ,     remove_columns=["idx", "sentence1", "sentence2"]    ,     load_from_cache_file=a__ )

  # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
  # transformers library
  _UpperCamelCase           =    tokenized_datasets.rename_column("label"    ,     "labels" )

  def collate_fn(a__ ):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    if accelerator.distributed_type == DistributedType.TPU:
      return tokenizer.pad(a__    ,     padding="max_length"    ,     max_length=128    ,     return_tensors="pt" )
    return tokenizer.pad(a__    ,     padding="longest"    ,     return_tensors="pt" )

  # Instantiate dataloaders.
  _UpperCamelCase           =    DataLoader(
      tokenized_datasets["train"]    ,     shuffle=a__    ,     collate_fn=a__    ,     batch_size=a__ )
  _UpperCamelCase           =    DataLoader(
      tokenized_datasets["validation"]    ,     shuffle=a__    ,     collate_fn=a__    ,     batch_size=a__ )

  return train_dataloader, eval_dataloader


def 				lowerCAmelCase__       (			a__    ,     a__ )      ->Dict:




  '''simple docstring'''

  _UpperCamelCase           =    Accelerator()

  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  _UpperCamelCase           =    config["lr"]
  _UpperCamelCase           =    int(config["num_epochs"] )
  _UpperCamelCase           =    int(config["seed"] )
  _UpperCamelCase           =    int(config["batch_size"] )
  _UpperCamelCase           =    args.model_name_or_path

  set_seed(a__ )
  _UpperCamelCase      ,						_UpperCamelCase           =    get_dataloaders(a__    ,     a__    ,     a__ )

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  _UpperCamelCase           =    AutoModelForSequenceClassification.from_pretrained(a__    ,     return_dict=a__ )

  # Instantiate optimizer
  _UpperCamelCase           =    (
      AdamW
      if accelerator.state.deepspeed_plugin is None
      or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
      else DummyOptim
  )
  _UpperCamelCase           =    optimizer_cls(params=model.parameters()    ,     lr=a__ )

  if accelerator.state.deepspeed_plugin is not None:
    _UpperCamelCase           =    accelerator.state.deepspeed_plugin.deepspeed_config[
        "gradient_accumulation_steps"
    ]
  else:
    _UpperCamelCase           =    1
  _UpperCamelCase           =    (len(a__ ) * num_epochs) // gradient_accumulation_steps

  # Instantiate scheduler
  if (
      accelerator.state.deepspeed_plugin is None
      or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
  ):
    _UpperCamelCase           =    get_linear_schedule_with_warmup(
        optimizer=a__    ,     num_warmup_steps=0    ,     num_training_steps=a__    ,     )
  else:
    _UpperCamelCase           =    DummyScheduler(a__    ,     total_num_steps=a__    ,     warmup_num_steps=0 )

  # Prepare everything
  # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
  # prepare method.
  _UpperCamelCase      ,						_UpperCamelCase      ,						_UpperCamelCase      ,						_UpperCamelCase      ,						_UpperCamelCase           =    accelerator.prepare(
      a__    ,     a__    ,     a__    ,     a__    ,     a__ )

  # We need to keep track of how many total steps we have iterated over
  _UpperCamelCase           =    0
  # We also need to keep track of the stating epoch so files are named properly
  _UpperCamelCase           =    0

  # Now we train the model
  _UpperCamelCase           =    evaluate.load("glue"    ,     "mrpc" )
  _UpperCamelCase           =    0
  _UpperCamelCase           =    {}
  for epoch in range(a__    ,     a__ ):
    model.train()
    for step, batch in enumerate(a__ ):
      _UpperCamelCase           =    model(**a__ )
      _UpperCamelCase           =    outputs.loss
      _UpperCamelCase           =    loss / gradient_accumulation_steps
      accelerator.backward(a__ )
      if step % gradient_accumulation_steps == 0:
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

      overall_step += 1

    model.eval()
    _UpperCamelCase           =    0
    for step, batch in enumerate(a__ ):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      batch.to(accelerator.device )
      with torch.no_grad():
        _UpperCamelCase           =    model(**a__ )
      _UpperCamelCase           =    outputs.logits.argmax(dim=-1 )
      # It is slightly faster to call this once, than multiple times
      _UpperCamelCase      ,						_UpperCamelCase           =    accelerator.gather(
          (predictions, batch["labels"]) )  # If we are in a multiprocess environment, the last batch has duplicates
      if accelerator.use_distributed:
        if step == len(a__ ) - 1:
          _UpperCamelCase           =    predictions[: len(eval_dataloader.dataset ) - samples_seen]
          _UpperCamelCase           =    references[: len(eval_dataloader.dataset ) - samples_seen]
        else:
          samples_seen += references.shape[0]
      metric.add_batch(
          predictions=a__    ,     references=a__    ,     )

    _UpperCamelCase           =    metric.compute()
    # Use accelerator.print to print only on the main process.
    accelerator.print(f'epoch {epoch}:'    ,     a__ )
    _UpperCamelCase           =    eval_metric["accuracy"]

    if best_performance < eval_metric["accuracy"]:
      _UpperCamelCase           =    eval_metric["accuracy"]

  if args.performance_lower_bound is not None:
    assert (
        args.performance_lower_bound <= best_performance
    ), f'Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'

  accelerator.wait_for_everyone()
  if accelerator.is_main_process:
    with open(os.path.join(args.output_dir    ,     "all_results.json" )    ,     "w" ) as f:
      json.dump(a__    ,     a__ )


def 				lowerCAmelCase__       (			)      ->Any:




  '''simple docstring'''

  _UpperCamelCase           =    argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage." )
  parser.add_argument(
      "--model_name_or_path"    ,     type=a__    ,     default="bert-base-cased"    ,     help="Path to pretrained model or model identifier from huggingface.co/models."    ,     required=a__    ,     )
  parser.add_argument(
      "--output_dir"    ,     type=a__    ,     default="."    ,     help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."    ,     )
  parser.add_argument(
      "--performance_lower_bound"    ,     type=a__    ,     default=a__    ,     help="Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value."    ,     )
  parser.add_argument(
      "--num_epochs"    ,     type=a__    ,     default=3    ,     help="Number of train epochs."    ,     )
  _UpperCamelCase           =    parser.parse_args()
  _UpperCamelCase           =    {"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
  training_function(a__    ,     a__ )


if __name__ == "__main__":
   main()





from typing import Optional
from urllib.parse import quote

import huggingface_hub as hfh
from packaging import version


def 				lowerCAmelCase__       (			a__    ,     a__    ,     a__ = None )      ->str:




  '''simple docstring'''

  if version.parse(hfh.__version__ ).release < version.parse("0.11.0" ).release:
    # old versions of hfh don't url-encode the file path
    _UpperCamelCase           =    quote(a__ )
  return hfh.hf_hub_url(a__    ,     a__    ,     repo_type="dataset"    ,     revision=a__ )




